ArticleJournal of nursing management2026
Identifying Nurses at Risk of Nursing Interruptions During Medication Administration Using Machine Learning: A Multicenter Cross-Sectional Study.
Article in Journal of nursing management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Identifying Nurses at Risk of Nursing Interruptions During Medication Administration Using Machine Learning: A Multicenter Cross-Sectional Study.Journal of nursing management · 2026Article
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11 authors.
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Abstract
BACKGROUND AND
aimsNursing interruptions during medication administration (NIMA) critically contribute to medication administration errors. However, validated prediction tools for NIMA risk assessment are unavailable. This study aimed to develop and internally validate three machine learning-based prediction models (logistic regression [LR], decision tree [DT], and Naive Bayes [NB]) for identifying nurses' individualized risk of NIMA.
methodsA total of 4758 Chinese nurses were recruited from 12 tertiary hospitals between November 2023 and January 2024. The outcome was defined as the occurrence of ≥ 1 NIMA during the latest work shift. We used univariate analysis and LR to identify predictors. Participants were randomly allocated to training (n = 3806; 80%) and internal validation (n = 952; 20%) sets. Three machine learning models were implemented in Python, with performance evaluated via 1000-iteration stratified bootstrapping. Key performance metrics included AUC, accuracy, recall, specificity, precision, F1-score, and G-mean. Models comparisons used the DeLong test. The best model was further assessed with the Hosmer-Lemeshow test and calibration curves.
resultsOverall, 52.1% of nurses experienced at least one NIMA event. Predictive features of NIMA included 18 factors, such as department type, marital status, shift type, general self-efficacy level, and the needs of multiple people. In the test set, AUCs ranged from 0.679 to 0.748. The LR model performed best, achieving the highest AUC of 0.748 (95% CI: 0.717-0.779), an accuracy of 0.694 (95% CI: 0.664-0.724), and other performance metrics, including precision, recall, specificity, F1-score, and G-mean. The calibration of the LR model was supported by the Hosmer-Lemeshow test (X
conclusionsThis study systematically evaluated multiple influencing factors of NIMA and developed three internally validated risk prediction models of NIMA. The LR-based nomogram and the web-based calculators showed the most consistent performance and may support risk stratification and targeted nursing management, pending external validation and feasibility assessment.
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